
# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

# Libraries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns


# To tune model, get different metric scores and split data
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score
from sklearn.metrics import f1_score,accuracy_score,recall_score,precision_score
from sklearn import metrics

# To build a logistic regression model
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# To oversample and undersample data
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler

#For making pipelines
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer

# To suppress the warnings
import warnings
warnings.filterwarnings("ignore")


#Loading the orginal training dataset
org_data = pd.read_csv('/Users/anshamohammed/Desktop/Drive G/specialised course/Feature_eng/Project/Train.csv.csv')
org_data


#Making a copy
df = org_data.copy()
org_data


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20000 entries, 0 to 19999
Data columns (total 41 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   V1      19982 non-null  float64
 1   V2      19982 non-null  float64
 2   V3      20000 non-null  float64
 3   V4      20000 non-null  float64
 4   V5      20000 non-null  float64
 5   V6      20000 non-null  float64
 6   V7      20000 non-null  float64
 7   V8      20000 non-null  float64
 8   V9      20000 non-null  float64
 9   V10     20000 non-null  float64
 10  V11     20000 non-null  float64
 11  V12     20000 non-null  float64
 12  V13     20000 non-null  float64
 13  V14     20000 non-null  float64
 14  V15     20000 non-null  float64
 15  V16     20000 non-null  float64
 16  V17     20000 non-null  float64
 17  V18     20000 non-null  float64
 18  V19     20000 non-null  float64
 19  V20     20000 non-null  float64
 20  V21     20000 non-null  float64
 21  V22     20000 non-null  float64
 22  V23     20000 non-null  float64
 23  V24     20000 non-null  float64
 24  V25     20000 non-null  float64
 25  V26     20000 non-null  float64
 26  V27     20000 non-null  float64
 27  V28     20000 non-null  float64
 28  V29     20000 non-null  float64
 29  V30     20000 non-null  float64
 30  V31     20000 non-null  float64
 31  V32     20000 non-null  float64
 32  V33     20000 non-null  float64
 33  V34     20000 non-null  float64
 34  V35     20000 non-null  float64
 35  V36     20000 non-null  float64
 36  V37     20000 non-null  float64
 37  V38     20000 non-null  float64
 38  V39     20000 non-null  float64
 39  V40     20000 non-null  float64
 40  Target  20000 non-null  int64  
dtypes: float64(40), int64(1)
memory usage: 6.3 MB


df.describe().T


df.Target.value_counts()

0    18890
1     1110
Name: Target, dtype: int64


df.Target.value_counts().values[0]/df.Target.value_counts().values.sum()

0.9445


df.Target.value_counts().values[1]/df.Target.value_counts().values.sum()

0.0555


df.isnull().sum()[df.isnull().sum() > 0]

V1    18
V2    18
dtype: int64


# function to plot a boxplot and a histogram along the same scale.


def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, hue = 'Target'
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


for feature in df.columns:
    histogram_boxplot(df, feature, figsize=(12, 7), kde=False, bins=None) ## Please change the dataframe name as you define while reading the data


plt.figure(figsize=(25,20))
sns.heatmap(df.corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")

<AxesSubplot: >


df.duplicated().sum()

0


df.isnull().sum()[df.isnull().sum() > 0]

V1    18
V2    18
dtype: int64


imputation_dict = {'V1': df.V1.median(), 'V2' : df.V2.mean()}
df.fillna(imputation_dict, inplace= True)


df.isnull().sum()

V1        0
V2        0
V3        0
V4        0
V5        0
V6        0
V7        0
V8        0
V9        0
V10       0
V11       0
V12       0
V13       0
V14       0
V15       0
V16       0
V17       0
V18       0
V19       0
V20       0
V21       0
V22       0
V23       0
V24       0
V25       0
V26       0
V27       0
V28       0
V29       0
V30       0
V31       0
V32       0
V33       0
V34       0
V35       0
V36       0
V37       0
V38       0
V39       0
V40       0
Target    0
dtype: int64


correlation = df.corr()


#Finding the correlated features
highly_correlated_cols = set()
for i in range(len(correlation.columns)):
    for j in range(i):
        if abs(correlation.iloc[i, j]) > 0.8:
            colname = correlation.columns[i]
            highly_correlated_cols.add(colname)
highly_correlated_cols

{'V14', 'V15', 'V16', 'V21', 'V29', 'V32'}


#Droping the corelated features
df.drop(highly_correlated_cols, axis = 1, inplace= True)


#again checking the correlation
plt.figure(figsize=(25,20))
sns.heatmap(df.corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")

<AxesSubplot: >


#creating a function for outlier treatment. Here using vinserisation technique
def outlier_tretment(df,column):
    cap = df[column].quantile(.75) + (1.5 * (df[column].quantile(.75) - df[column].quantile(.25)))
    floor = df[column].quantile(.25)  - (1.5 * (df[column].quantile(.75) - df[column].quantile(.25)))
    df.loc[df[column] >= cap,column] = cap
    df.loc[df[column] <= floor,column] = floor
    print('{} number ourliers are floored to {} and {} number ourliers are capped to {} for {}'.format(sum(df[column] >= cap),cap,sum(df[column] <= floor),floor,column))
    return df


df.columns.drop('Target')

Index(['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11',
       'V12', 'V13', 'V17', 'V18', 'V19', 'V20', 'V22', 'V23', 'V24', 'V25',
       'V26', 'V27', 'V28', 'V30', 'V31', 'V33', 'V34', 'V35', 'V36', 'V37',
       'V38', 'V39', 'V40'],
      dtype='object')


#Doing the Outlier treatment for all columns
for i in df.columns.drop('Target'):
    df = outlier_tretment(df,i)
df

204 number ourliers are floored to 8.697038993 and 10 number ourliers are capped to -9.595468691
85 number ourliers are floored to 8.812472241125 and 98 number ourliers are capped to -7.907372703875001
215 number ourliers are floored to 11.105121678 and 60 number ourliers are capped to -6.332096354
139 number ourliers are floored to 8.848026312624999 and 89 number ourliers are capped to -9.065071370375
81 number ourliers are floored to 5.654610273874999 and 32 number ourliers are capped to -5.849736979125
65 number ourliers are floored to 4.471681297875 and 90 number ourliers are capped to -6.4385896071249995
203 number ourliers are floored to 3.6056262381250006 and 88 number ourliers are capped to -5.412857980875001
32 number ourliers are floored to 8.271409967 and 159 number ourliers are capped to -9.191109786999998
93 number ourliers are floored to 5.76546797325 and 55 number ourliers are capped to -5.85123781075
53 number ourliers are floored to 5.809430781 and 161 number ourliers are capped to -5.743597170999999
136 number ourliers are floored to 6.180870850124999 and 122 number ourliers are capped to -9.984368858875
82 number ourliers are floored to 9.523405883875 and 66 number ourliers are capped to -6.348465309124999
108 number ourliers are floored to 8.985032979875 and 195 number ourliers are capped to -5.748692261125
99 number ourliers are floored to 8.495293339749999 and 197 number ourliers are capped to -8.642152822249999
480 number ourliers are floored to 7.035300772125 and 251 number ourliers are capped to -4.8674477688749995
42 number ourliers are floored to 10.308498732124999 and 107 number ourliers are capped to -7.865368428875
66 number ourliers are floored to 9.930359781625 and 87 number ourliers are capped to -9.850940157375
89 number ourliers are floored to 5.24117474725 and 156 number ourliers are capped to -3.33370782275
35 number ourliers are floored to 10.777507935374999 and 59 number ourliers are capped to -11.424514369625
221 number ourliers are floored to 11.067031360749999 and 86 number ourliers are capped to -8.989118585249999
40 number ourliers are floored to 5.54054729775 and 74 number ourliers are capped to -5.50861358825
99 number ourliers are floored to 10.83188680575 and 143 number ourliers are capped to -7.039712204250001
155 number ourliers are floored to 10.951426229875 and 23 number ourliers are capped to -12.414572493125
106 number ourliers are floored to 4.196537342625 and 75 number ourliers are capped to -5.991870726375
34 number ourliers are floored to 7.891242076125001 and 212 number ourliers are capped to -7.722126908875
118 number ourliers are floored to 9.553376783500001 and 111 number ourliers are capped to -8.6404610585
225 number ourliers are floored to 9.002119060375 and 158 number ourliers are capped to -8.989842008624999
249 number ourliers are floored to 6.7978123625 and 554 number ourliers are capped to -7.497861389500001
182 number ourliers are floored to 9.656608620000002 and 133 number ourliers are capped to -5.256060092000001
127 number ourliers are floored to 11.37556147175 and 134 number ourliers are capped to -8.33543197025
134 number ourliers are floored to 4.8225600135 and 6 number ourliers are capped to -4.9028459125
81 number ourliers are floored to 10.179954516625 and 84 number ourliers are capped to -10.888193428375
85 number ourliers are floored to 5.55222571925 and 110 number ourliers are capped to -3.76693592675
91 number ourliers are floored to 7.2100333027499985 and 46 number ourliers are capped to -9.030329529249999


# outlier detection using boxplot


plt.figure(figsize=(15, 10))

for i, variable in enumerate(df.columns.drop('Target')):
    plt.subplot(7, 5, i + 1)
    sns.boxplot(data=df, x=variable)
    plt.tight_layout(pad=2)

plt.show()


plt.figure(figsize=(15, 10))

for i, variable in enumerate(df.columns.drop('Target')):
    plt.subplot(7, 5, i + 1)
    sns.histplot(data=df, x=variable)
    plt.tight_layout(pad=2)

plt.show()


X_train, X_test, y_train, y_test = train_test_split(df.drop('Target',axis = 1), df['Target'], train_size= .85, random_state= 10)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, train_size= .8, random_state= 10)


X_train.shape

(13600, 34)


X_val.shape

(3400, 34)


X_test.shape

(3000, 34)


# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {
            "Accuracy": acc,
            "Recall": recall,
            "Precision": precision,
            "F1": f1
            
        },
        index=[0],
    )

    return df_perf


# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)


models = []  # Empty list to store all the models

# Appending models into the list
models.append(("dtree", DecisionTreeClassifier(random_state=1)))
models.append(("logreg", LogisticRegression(random_state=1)))
models.append(("Randomforest", RandomForestClassifier(random_state=1)))
models.append(("Bagging_class", BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=1))))
models.append(("Adaboost_class", AdaBoostClassifier(base_estimator=DecisionTreeClassifier(random_state=1))))
models.append(("SVM", SVC(kernel='linear', random_state=1)))


results1 = []  # Empty list to store all model's CV scores
names = []  # Empty list to store name of the models


# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation performance on training dataset:" "\n")

for name, model in models:
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 10
    cv_result = cross_val_score(
        estimator=model, X=X_train, y=y_train, scoring=scorer, cv=kfold
    )
    results1.append(cv_result)
    names.append(name)
    print("{}: {}".format(name, cv_result.mean()))

print("\n" "Validation Performance:" "\n")

for name, model in models:
    model.fit(X_train, y_train)
    scores = recall_score(y_val, model.predict(X_val))
    print("{}: {}".format(name, scores))

Cross-Validation performance on training dataset:

dtree: 0.7236842105263157
logreg: 0.5210526315789473
Randomforest: 0.7092105263157895
Bagging_class: 0.7105263157894738
Adaboost_class: 0.7263157894736842
SVM: 0.48552631578947364

Validation Performance:

dtree: 0.6720430107526881
logreg: 0.44623655913978494
Randomforest: 0.6612903225806451
Bagging_class: 0.6182795698924731
Adaboost_class: 0.6559139784946236
SVM: 0.43010752688172044


# Synthetic Minority Over Sampling Technique
sm = SMOTE(sampling_strategy=1, k_neighbors=10, random_state=1)
X_train_over, y_train_over = sm.fit_resample(X_train, y_train)


results2 = []  # Empty list to store all model's CV scores
names = []  # Empty list to store name of the models

# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation performance on training dataset:" "\n")

for name, model in models:
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 5
    cv_result = cross_val_score(
        estimator=model, X=X_train_over, y=y_train_over, scoring=scorer, cv=kfold
    )
    results2.append(cv_result)
    names.append(name)
    print("{}: {}".format(name, cv_result.mean()))

print("\n" "Validation Performance:" "\n")

for name, model in models:
    model.fit(X_train_over, y_train_over)
    scores = recall_score(y_val, model.predict(X_val))
    print("{}: {}".format(name, scores))

Cross-Validation performance on training dataset:

dtree: 0.9611370716510905
logreg: 0.8812305295950156
Randomforest: 0.9758566978193148
Bagging_class: 0.9658878504672896
Adaboost_class: 0.9607476635514018
SVM: 0.8801401869158878

Validation Performance:

dtree: 0.7580645161290323
logreg: 0.8387096774193549
Randomforest: 0.8279569892473119
Bagging_class: 0.7741935483870968
Adaboost_class: 0.7473118279569892
SVM: 0.8333333333333334


# Random undersampler for under sampling the data
rus = RandomUnderSampler(random_state=1, sampling_strategy=1)
X_train_un, y_train_un = rus.fit_resample(X_train, y_train)


results3 = []  # Empty list to store all model's CV scores
names = []  # Empty list to store name of the models


# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation performance on training dataset:" "\n")

for name, model in models:
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 5
    cv_result = cross_val_score(
        estimator=model, X=X_train_un, y=y_train_un, scoring=scorer, cv=kfold
    )
    results3.append(cv_result)
    names.append(name)
    print("{}: {}".format(name, cv_result.mean()))

print("\n" "Validation Performance:" "\n")

for name, model in models:
    model.fit(X_train_un, y_train_un)
    scores = recall_score(y_val, model.predict(X_val))
    print("{}: {}".format(name, scores))

Cross-Validation performance on training dataset:

dtree: 0.8486842105263157
logreg: 0.8631578947368421
Randomforest: 0.8934210526315789
Bagging_class: 0.8578947368421053
Adaboost_class: 0.844736842105263
SVM: 0.8605263157894736

Validation Performance:

dtree: 0.8225806451612904
logreg: 0.8387096774193549
Randomforest: 0.8655913978494624
Bagging_class: 0.8333333333333334
Adaboost_class: 0.8279569892473119
SVM: 0.8333333333333334


param_grid_1 = {
    'max_depth': np.arange(2,6), 
    'min_samples_leaf': [1, 4, 7],
    'max_leaf_nodes' : [10, 15],
    'min_impurity_decrease': [0.0001,0.001]
}

param_grid_2 = {'C': np.arange(0.1,1.1,0.1)}

param_grid_3 = {
    "n_estimators": [200,250,300],
    "min_samples_leaf": np.arange(1, 4),
    "max_features": [np.arange(0.3, 0.6, 0.1),'sqrt'],
    "max_samples": np.arange(0.4, 0.7, 0.1)
}

param_grid_4 = {
    'max_samples': [0.8,0.9,1], 
    'max_features': [0.7,0.8,0.9],
    'n_estimators' : [30,50,70],
}

param_grid_5 = {
    "n_estimators": [100, 150, 200],
    "learning_rate": [0.2, 0.05],
    "base_estimator": [DecisionTreeClassifier(max_depth=1, random_state=1), DecisionTreeClassifier(max_depth=2, random_state=1), DecisionTreeClassifier(max_depth=3, random_state=1),
    ]
}

param_grid_6 = {
    'C': [0.1, 1, 10],          # Regularization parameter
    'kernel': ['linear', 'rbf', 'poly'],  # Kernel type
    'degree': [2, 3, 4],        # Degree of the polynomial kernel (only for 'poly' kernel)
    'gamma': ['scale', 'auto'], # Kernel coefficient (only for 'rbf' and 'poly' kernels)
}


param_grid = [param_grid_1,param_grid_2,param_grid_3,param_grid_4,param_grid_5,param_grid_6]


models_rscv_1 = []  # Empty list to store all the models

# Appending models into the list
models_rscv_1.append((param_grid_1,"dtree", DecisionTreeClassifier(random_state=1)))
models_rscv_1.append((param_grid_3,"Randomforest", RandomForestClassifier(random_state=1)))
models_rscv_1.append((param_grid_5,"Adaboost_class", AdaBoostClassifier(base_estimator=DecisionTreeClassifier(random_state=1))))


best_model_normal = []
for param, name, model in models_rscv_1:
#Calling RandomizedSearchCV
    randomized_cv = RandomizedSearchCV(estimator=model, param_distributions=param, n_iter=50, n_jobs = -1, scoring=scorer, cv=5, random_state=1)

    #Fitting parameters in RandomizedSearchCV
    randomized_cv.fit(X_train,y_train)

    best_params_1 = randomized_cv.best_params_
    best_model_1 = randomized_cv.best_estimator_
    best_model_normal.append((name,best_model_1))

    model_check = best_model_1.fit(X_train,y_train)
    scores = recall_score(y_val, model_check.predict(X_val))
    
    print(" The {} model's Best parameters are {} with CV score={}:" .format(name, randomized_cv.best_params_,randomized_cv.best_score_))
    print("Validation score of {}: {}".format(name, scores))

 The dtree model's Best parameters are {'min_samples_leaf': 1, 'min_impurity_decrease': 0.0001, 'max_leaf_nodes': 15, 'max_depth': 5} with CV score=0.525:
Validation score of dtree: 0.489247311827957
 The Randomforest model's Best parameters are {'n_estimators': 300, 'min_samples_leaf': 1, 'max_samples': 0.6, 'max_features': 'sqrt'} with CV score=0.6881578947368421:
Validation score of Randomforest: 0.6397849462365591

/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(

 The Adaboost_class model's Best parameters are {'n_estimators': 200, 'learning_rate': 0.2, 'base_estimator': DecisionTreeClassifier(max_depth=3, random_state=1)} with CV score=0.7592105263157894:
Validation score of Adaboost_class: 0.7096774193548387


models_rscv_2 = []  # Empty list to store all the models

# Appending models into the list
models_rscv_2.append((param_grid_3,"Randomforest", RandomForestClassifier(random_state=1)))
models_rscv_2.append((param_grid_2,"logreg", LogisticRegression(random_state=1)))
models_rscv_2.append((param_grid_6,"SVM", SVC(random_state=1)))


best_model_over = []
for param, name, model in models_rscv_2:
#Calling RandomizedSearchCV
    randomized_cv = RandomizedSearchCV(estimator=model, param_distributions=param, n_iter=50, n_jobs = -1, scoring=scorer, cv=5, random_state=1)

    #Fitting parameters in RandomizedSearchCV
    randomized_cv.fit(X_train,y_train)

    best_params_1 = randomized_cv.best_params_
    best_model_1 = randomized_cv.best_estimator_
    best_model_over.append((name,best_model_1))

    model_check = best_model_1.fit(X_train,y_train)
    scores = recall_score(y_val, model_check.predict(X_val))
    
    print(" The {} model's Best parameters are {} with CV score={}:" .format(name, randomized_cv.best_params_,randomized_cv.best_score_))
    print("Validation score of {}: {}".format(name, scores))

 The Randomforest model's Best parameters are {'n_estimators': 300, 'min_samples_leaf': 1, 'max_samples': 0.6, 'max_features': 'sqrt'} with CV score=0.6881578947368421:
Validation score of Randomforest: 0.6397849462365591
 The logreg model's Best parameters are {'C': 0.1} with CV score=0.5131578947368421:
Validation score of logreg: 0.44623655913978494
 The SVM model's Best parameters are {'kernel': 'rbf', 'gamma': 'scale', 'degree': 4, 'C': 10} with CV score=0.8605263157894736:
Validation score of SVM: 0.8225806451612904


models_rscv_3 = []  # Empty list to store all the models

# Appending models into the list
models_rscv_3.append((param_grid_2,"logreg", LogisticRegression(random_state=1)))
models_rscv_3.append((param_grid_3,"Randomforest", RandomForestClassifier(random_state=1)))
models_rscv_3.append((param_grid_4,"Bagging_class", BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=1))))


best_model_under = []
for param, name, model in models_rscv_3:
#Calling RandomizedSearchCV
    randomized_cv = RandomizedSearchCV(estimator=model, param_distributions=param, n_iter=50, n_jobs = -1, scoring=scorer, cv=5, random_state=1)

    #Fitting parameters in RandomizedSearchCV
    randomized_cv.fit(X_train_un,y_train_un)

    best_params_1 = randomized_cv.best_params_
    best_model_1 = randomized_cv.best_estimator_
    best_model_under.append((name,best_model_1))
    
    model_check = best_model_1.fit(X_train_un,y_train_un)
    scores = recall_score(y_val, model_check.predict(X_val))
    
    print(" The {} model's Best parameters are {} with CV score={}:" .format(name, randomized_cv.best_params_,randomized_cv.best_score_))
    print("Validation score of {}: {}".format(name, scores))

 The logreg model's Best parameters are {'C': 0.30000000000000004} with CV score=0.868421052631579:
Validation score of logreg: 0.8387096774193549
 The Randomforest model's Best parameters are {'n_estimators': 300, 'min_samples_leaf': 1, 'max_samples': 0.6, 'max_features': 'sqrt'} with CV score=0.8960526315789472:
Validation score of Randomforest: 0.8655913978494624

/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
/opt/homebrew/lib/python3.10/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(

 The Bagging_class model's Best parameters are {'n_estimators': 70, 'max_samples': 0.8, 'max_features': 0.8} with CV score=0.8907894736842106:
Validation score of Bagging_class: 0.8548387096774194


# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(models, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    
    
    accuracy_list = []
    recall_list = []
    precision_list = []
    f1_score_list = []
    index_list = []

    for i in range (len(models)):
        pred = models[i].predict(predictors)
        acc = accuracy_score(target, pred)  # to compute Accuracy
        accuracy_list.append(acc)
    
        recall = recall_score(target, pred)  # to compute Recall
        recall_list.append(recall)
    
        precision = precision_score(target, pred)  # to compute Precision
        precision_list.append(precision)
    
        f1 = f1_score(target, pred)  # to compute F1-score
        f1_score_list.append(f1)
        
        model_name = 'Model_' + str(i)
        index_list.append(model_name)
    

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {
            "Accuracy": accuracy_list,
            "Recall": recall_list,
            "Precision": precision_list,
            "F1": f1_score_list
            
        },
        index= index_list,
    )

    return df_perf


all_9_models = best_model_normal + best_model_over + best_model_under


all_9_models[0][1]

DecisionTreeClassifier(max_depth=5, max_leaf_nodes=15,
                       min_impurity_decrease=0.0001, random_state=1)

DecisionTreeClassifier(max_depth=5, max_leaf_nodes=15,
                       min_impurity_decrease=0.0001, random_state=1)


all_9_models_only = [i[1] for i in all_9_models]


all_9_models_only

[DecisionTreeClassifier(max_depth=5, max_leaf_nodes=15,
                        min_impurity_decrease=0.0001, random_state=1),
 RandomForestClassifier(max_samples=0.6, n_estimators=300, random_state=1),
 AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=3,
                                                          random_state=1),
                    learning_rate=0.2, n_estimators=200),
 RandomForestClassifier(max_samples=0.6, n_estimators=300, random_state=1),
 LogisticRegression(C=0.1, random_state=1),
 SVC(C=10, degree=4, random_state=1),
 LogisticRegression(C=0.30000000000000004, random_state=1),
 RandomForestClassifier(max_samples=0.6, n_estimators=300, random_state=1),
 BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=1),
                   max_features=0.8, max_samples=0.8, n_estimators=70)]


model_performance_classification_sklearn(all_9_models_only, X_train, y_train)


model_performance_classification_sklearn(all_9_models_only, X_val, y_val)


model_performance_classification_sklearn(all_9_models_only, X_test, y_test)


df_test = pd.read_csv('/Users/anshamohammed/Desktop/Drive G/specialised course/Feature_eng/Project/Test.csv.csv')
df_test


df_test.isnull().sum()

V1        5
V2        6
V3        0
V4        0
V5        0
V6        0
V7        0
V8        0
V9        0
V10       0
V11       0
V12       0
V13       0
V14       0
V15       0
V16       0
V17       0
V18       0
V19       0
V20       0
V21       0
V22       0
V23       0
V24       0
V25       0
V26       0
V27       0
V28       0
V29       0
V30       0
V31       0
V32       0
V33       0
V34       0
V35       0
V36       0
V37       0
V38       0
V39       0
V40       0
Target    0
dtype: int64


imputation_dict = {'V1': df_test.V1.median(), 'V2' : df_test.V2.mean()}
df_test.fillna(imputation_dict, inplace= True)


df_test.drop(highly_correlated_cols, axis = 1, inplace= True)


X = df_test.drop('Target', axis = 1)
y = df_test['Target']


model_performance_classification_sklearn(all_9_models_only, X, y)


# to create pipeline and make_pipeline
from sklearn.pipeline import Pipeline, make_pipeline


#Selecting the best Model
all_9_models_only[5]

SVC(C=10, degree=4, random_state=1)

SVC(C=10, degree=4, random_state=1)


#Making imputer pipeline
numeric_transformer1 = Pipeline(steps=[("imputer", SimpleImputer(strategy="median"))])
numeric_transformer2 = Pipeline(steps=[("imputer", SimpleImputer(strategy="mean"))])


def drop_cols (df, highly_correlated_cols):
    return df.drop(highly_correlated_cols, axis = 1).transform(df)


#Making imputer Column transformer
preprocessor = ColumnTransformer(
    transformers=[
        ("num1", numeric_transformer1, ['V1']),
        ("num2", numeric_transformer2, ['V2']),
    ],remainder="passthrough",)


preprocessor

ColumnTransformer(remainder='passthrough',
                  transformers=[('num1',
                                 Pipeline(steps=[('imputer',
                                                  SimpleImputer(strategy='median'))]),
                                 ['V1']),
                                ('num2',
                                 Pipeline(steps=[('imputer', SimpleImputer())]),
                                 ['V2'])])

ColumnTransformer(remainder='passthrough',
                  transformers=[('num1',
                                 Pipeline(steps=[('imputer',
                                                  SimpleImputer(strategy='median'))]),
                                 ['V1']),
                                ('num2',
                                 Pipeline(steps=[('imputer', SimpleImputer())]),
                                 ['V2'])])

['V1']

SimpleImputer(strategy='median')

['V2']

SimpleImputer()

passthrough


# Creating new pipeline with best parameters
pipe = Pipeline(
    steps=[("pre", preprocessor),
           ("LGR",all_9_models_only[5])])


# Fit the model on training data
pipe.fit(X_train, y_train)

Pipeline(steps=[('pre',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num1',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='median'))]),
                                                  ['V1']),
                                                 ('num2',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer())]),
                                                  ['V2'])])),
                ('LGR', SVC(C=10, degree=4, random_state=1))])

Pipeline(steps=[('pre',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num1',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='median'))]),
                                                  ['V1']),
                                                 ('num2',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer())]),
                                                  ['V2'])])),
                ('LGR', SVC(C=10, degree=4, random_state=1))])

ColumnTransformer(remainder='passthrough',
                  transformers=[('num1',
                                 Pipeline(steps=[('imputer',
                                                  SimpleImputer(strategy='median'))]),
                                 ['V1']),
                                ('num2',
                                 Pipeline(steps=[('imputer', SimpleImputer())]),
                                 ['V2'])])

['V1']

SimpleImputer(strategy='median')

['V2']

SimpleImputer()

['V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V17', 'V18', 'V19', 'V20', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V30', 'V31', 'V33', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', 'V40']

passthrough

SVC(C=10, degree=4, random_state=1)


recall = recall_score(y, pipe.predict(X))  # to compute Recall
recall

0.8439716312056738

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	...	V32	V33	V34	V35	V36	V37	V38	V39	V40	Target
0	-4.464606	-4.679129	3.101546	0.506130	-0.221083	-2.032511	-2.910870	0.050714	-1.522351	3.761892	...	3.059700	-1.690440	2.846296	2.235198	6.667486	0.443809	-2.369169	2.950578	-3.480324	0
1	3.365912	3.653381	0.909671	-1.367528	0.332016	2.358938	0.732600	-4.332135	0.565695	-0.101080	...	-1.795474	3.032780	-2.467514	1.894599	-2.297780	-1.731048	5.908837	-0.386345	0.616242	0
2	-3.831843	-5.824444	0.634031	-2.418815	-1.773827	1.016824	-2.098941	-3.173204	-2.081860	5.392621	...	-0.257101	0.803550	4.086219	2.292138	5.360850	0.351993	2.940021	3.839160	-4.309402	0
3	1.618098	1.888342	7.046143	-1.147285	0.083080	-1.529780	0.207309	-2.493629	0.344926	2.118578	...	-3.584425	-2.577474	1.363769	0.622714	5.550100	-1.526796	0.138853	3.101430	-1.277378	0
4	-0.111440	3.872488	-3.758361	-2.982897	3.792714	0.544960	0.205433	4.848994	-1.854920	-6.220023	...	8.265896	6.629213	-10.068689	1.222987	-3.229763	1.686909	-2.163896	-3.644622	6.510338	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
19995	-2.071318	-1.088279	-0.796174	-3.011720	-2.287540	2.807310	0.481428	0.105171	-0.586599	-2.899398	...	-8.273996	5.745013	0.589014	-0.649988	-3.043174	2.216461	0.608723	0.178193	2.927755	1
19996	2.890264	2.483069	5.643919	0.937053	-1.380870	0.412051	-1.593386	-5.762498	2.150096	0.272302	...	-4.159092	1.181466	-0.742412	5.368979	-0.693028	-1.668971	3.659954	0.819863	-1.987265	0
19997	-3.896979	-3.942407	-0.351364	-2.417462	1.107546	-1.527623	-3.519882	2.054792	-0.233996	-0.357687	...	7.112162	1.476080	-3.953710	1.855555	5.029209	2.082588	-6.409304	1.477138	-0.874148	0
19998	-3.187322	-10.051662	5.695955	-4.370053	-5.354758	-1.873044	-3.947210	0.679420	-2.389254	5.456756	...	0.402812	3.163661	3.752095	8.529894	8.450626	0.203958	-7.129918	4.249394	-6.112267	0
19999	-2.686903	1.961187	6.137088	2.600133	2.657241	-4.290882	-2.344267	0.974004	-1.027462	0.497421	...	6.620811	-1.988786	-1.348901	3.951801	5.449706	-0.455411	-2.202056	1.678229	-1.974413	0

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	...	V32	V33	V34	V35	V36	V37	V38	V39	V40	Target
0	-4.464606	-4.679129	3.101546	0.506130	-0.221083	-2.032511	-2.910870	0.050714	-1.522351	3.761892	...	3.059700	-1.690440	2.846296	2.235198	6.667486	0.443809	-2.369169	2.950578	-3.480324	0
1	3.365912	3.653381	0.909671	-1.367528	0.332016	2.358938	0.732600	-4.332135	0.565695	-0.101080	...	-1.795474	3.032780	-2.467514	1.894599	-2.297780	-1.731048	5.908837	-0.386345	0.616242	0
2	-3.831843	-5.824444	0.634031	-2.418815	-1.773827	1.016824	-2.098941	-3.173204	-2.081860	5.392621	...	-0.257101	0.803550	4.086219	2.292138	5.360850	0.351993	2.940021	3.839160	-4.309402	0
3	1.618098	1.888342	7.046143	-1.147285	0.083080	-1.529780	0.207309	-2.493629	0.344926	2.118578	...	-3.584425	-2.577474	1.363769	0.622714	5.550100	-1.526796	0.138853	3.101430	-1.277378	0
4	-0.111440	3.872488	-3.758361	-2.982897	3.792714	0.544960	0.205433	4.848994	-1.854920	-6.220023	...	8.265896	6.629213	-10.068689	1.222987	-3.229763	1.686909	-2.163896	-3.644622	6.510338	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
19995	-2.071318	-1.088279	-0.796174	-3.011720	-2.287540	2.807310	0.481428	0.105171	-0.586599	-2.899398	...	-8.273996	5.745013	0.589014	-0.649988	-3.043174	2.216461	0.608723	0.178193	2.927755	1
19996	2.890264	2.483069	5.643919	0.937053	-1.380870	0.412051	-1.593386	-5.762498	2.150096	0.272302	...	-4.159092	1.181466	-0.742412	5.368979	-0.693028	-1.668971	3.659954	0.819863	-1.987265	0
19997	-3.896979	-3.942407	-0.351364	-2.417462	1.107546	-1.527623	-3.519882	2.054792	-0.233996	-0.357687	...	7.112162	1.476080	-3.953710	1.855555	5.029209	2.082588	-6.409304	1.477138	-0.874148	0
19998	-3.187322	-10.051662	5.695955	-4.370053	-5.354758	-1.873044	-3.947210	0.679420	-2.389254	5.456756	...	0.402812	3.163661	3.752095	8.529894	8.450626	0.203958	-7.129918	4.249394	-6.112267	0
19999	-2.686903	1.961187	6.137088	2.600133	2.657241	-4.290882	-2.344267	0.974004	-1.027462	0.497421	...	6.620811	-1.988786	-1.348901	3.951801	5.449706	-0.455411	-2.202056	1.678229	-1.974413	0

	count	mean	std	min	25%	50%	75%	max
V1	19982.0	-0.271996	3.441625	-11.876451	-2.737146	-0.747917	1.840112	15.493002
V2	19982.0	0.440430	3.150784	-12.319951	-1.640674	0.471536	2.543967	13.089269
V3	20000.0	2.484699	3.388963	-10.708139	0.206860	2.255786	4.566165	17.090919
V4	20000.0	-0.083152	3.431595	-15.082052	-2.347660	-0.135241	2.130615	13.236381
V5	20000.0	-0.053752	2.104801	-8.603361	-1.535607	-0.101952	1.340480	8.133797
V6	20000.0	-0.995443	2.040970	-10.227147	-2.347238	-1.000515	0.380330	6.975847
V7	20000.0	-0.879325	1.761626	-7.949681	-2.030926	-0.917179	0.223695	8.006091
V8	20000.0	-0.548195	3.295756	-15.657561	-2.642665	-0.389085	1.722965	11.679495
V9	20000.0	-0.016808	2.160568	-8.596313	-1.494973	-0.067597	1.409203	8.137580
V10	20000.0	-0.012998	2.193201	-9.853957	-1.411212	0.100973	1.477045	8.108472
V11	20000.0	-1.895393	3.124322	-14.832058	-3.922404	-1.921237	0.118906	11.826433
V12	20000.0	1.604825	2.930454	-12.948007	-0.396514	1.507841	3.571454	15.080698
V13	20000.0	1.580486	2.874658	-13.228247	-0.223545	1.637185	3.459886	15.419616
V14	20000.0	-0.950632	1.789651	-7.738593	-2.170741	-0.957163	0.270677	5.670664
V15	20000.0	-2.414993	3.354974	-16.416606	-4.415322	-2.382617	-0.359052	12.246455
V16	20000.0	-2.925225	4.221717	-20.374158	-5.634240	-2.682705	-0.095046	13.583212
V17	20000.0	-0.134261	3.345462	-14.091184	-2.215611	-0.014580	2.068751	16.756432
V18	20000.0	1.189347	2.592276	-11.643994	-0.403917	0.883398	2.571770	13.179863
V19	20000.0	1.181808	3.396925	-13.491784	-1.050168	1.279061	3.493299	13.237742
V20	20000.0	0.023608	3.669477	-13.922659	-2.432953	0.033415	2.512372	16.052339
V21	20000.0	-3.611252	3.567690	-17.956231	-5.930360	-3.532888	-1.265884	13.840473
V22	20000.0	0.951835	1.651547	-10.122095	-0.118127	0.974687	2.025594	7.409856
V23	20000.0	-0.366116	4.031860	-14.866128	-3.098756	-0.262093	2.451750	14.458734
V24	20000.0	1.134389	3.912069	-16.387147	-1.468062	0.969048	3.545975	17.163291
V25	20000.0	-0.002186	2.016740	-8.228266	-1.365178	0.025050	1.397112	8.223389
V26	20000.0	1.873785	3.435137	-11.834271	-0.337863	1.950531	4.130037	16.836410
V27	20000.0	-0.612413	4.368847	-14.904939	-3.652323	-0.884894	2.189177	17.560404
V28	20000.0	-0.883218	1.917713	-9.269489	-2.171218	-0.891073	0.375884	6.527643
V29	20000.0	-0.985625	2.684365	-12.579469	-2.787443	-1.176181	0.629773	10.722055
V30	20000.0	-0.015534	3.005258	-14.796047	-1.867114	0.184346	2.036229	12.505812
V31	20000.0	0.486842	3.461384	-13.722760	-1.817772	0.490304	2.730688	17.255090
V32	20000.0	0.303799	5.500400	-19.876502	-3.420469	0.052073	3.761722	23.633187
V33	20000.0	0.049825	3.575285	-16.898353	-2.242857	-0.066249	2.255134	16.692486
V34	20000.0	-0.462702	3.183841	-17.985094	-2.136984	-0.255008	1.436935	14.358213
V35	20000.0	2.229620	2.937102	-15.349803	0.336191	2.098633	4.064358	15.291065
V36	20000.0	1.514809	3.800860	-14.833178	-0.943809	1.566526	3.983939	19.329576
V37	20000.0	0.011316	1.788165	-5.478350	-1.255819	-0.128435	1.175533	7.467006
V38	20000.0	-0.344025	3.948147	-17.375002	-2.987638	-0.316849	2.279399	15.289923
V39	20000.0	0.890653	1.753054	-6.438880	-0.272250	0.919261	2.057540	7.759877
V40	20000.0	-0.875630	3.012155	-11.023935	-2.940193	-0.920806	1.119897	10.654265
Target	20000.0	0.055500	0.228959	0.000000	0.000000	0.000000	0.000000	1.000000

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	...	V31	V33	V34	V35	V36	V37	V38	V39	V40	Target
0	-4.464606	-4.679129	3.101546	0.506130	-0.221083	-2.032511	-2.910870	0.050714	-1.522351	3.761892	...	1.667098	-1.690440	2.846296	2.235198	6.667486	0.443809	-2.369169	2.950578	-3.480324	0
1	3.365912	3.653381	0.909671	-1.367528	0.332016	2.358938	0.732600	-4.332135	0.565695	-0.101080	...	0.024883	3.032780	-2.467514	1.894599	-2.297780	-1.731048	5.908837	-0.386345	0.616242	0
2	-3.831843	-5.824444	0.634031	-2.418815	-1.773827	1.016824	-2.098941	-3.173204	-2.081860	5.392621	...	-1.600395	0.803550	4.086219	2.292138	5.360850	0.351993	2.940021	3.839160	-4.309402	0
3	1.618098	1.888342	7.046143	-1.147285	0.083080	-1.529780	0.207309	-2.493629	0.344926	2.118578	...	4.948770	-2.577474	1.363769	0.622714	5.550100	-1.526796	0.138853	3.101430	-1.277378	0
4	-0.111440	3.872488	-3.758361	-2.982897	3.792714	0.544960	0.205433	4.848994	-1.854920	-5.743597	...	2.044184	6.629213	-7.497861	1.222987	-3.229763	1.686909	-2.163896	-3.644622	6.510338	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
19995	-2.071318	-1.088279	-0.796174	-3.011720	-2.287540	2.807310	0.481428	0.105171	-0.586599	-2.899398	...	-3.938493	5.745013	0.589014	-0.649988	-3.043174	2.216461	0.608723	0.178193	2.927755	1
19996	2.890264	2.483069	5.643919	0.937053	-1.380870	0.412051	-1.593386	-5.762498	2.150096	0.272302	...	-1.088553	1.181466	-0.742412	5.368979	-0.693028	-1.668971	3.659954	0.819863	-1.987265	0
19997	-3.896979	-3.942407	-0.351364	-2.417462	1.107546	-1.527623	-3.519882	2.054792	-0.233996	-0.357687	...	0.981858	1.476080	-3.953710	1.855555	5.029209	2.082588	-6.409304	1.477138	-0.874148	0
19998	-3.187322	-7.907373	5.695955	-4.370053	-5.354758	-1.873044	-3.947210	0.679420	-2.389254	5.456756	...	1.914766	3.163661	3.752095	8.529894	8.450626	0.203958	-7.129918	4.249394	-6.112267	0
19999	-2.686903	1.961187	6.137088	2.600133	2.657241	-4.290882	-2.344267	0.974004	-1.027462	0.497421	...	4.674280	-1.988786	-1.348901	3.951801	5.449706	-0.455411	-2.202056	1.678229	-1.974413	0

	Accuracy	Recall	Precision	F1
Model_0	0.974779	0.590789	0.933472	0.723610
Model_1	0.994706	0.907895	0.997110	0.950413
Model_2	0.999412	0.989474	1.000000	0.994709
Model_3	0.994706	0.907895	0.997110	0.950413
Model_4	0.968750	0.517105	0.871397	0.649050
Model_5	0.993971	0.893421	0.998529	0.943056
Model_6	0.865515	0.865789	0.275891	0.418442
Model_7	0.947574	0.976316	0.516354	0.675467
Model_8	0.948603	0.994737	0.521020	0.683853

Problem Statement¶

Business Context¶

Objective¶

Data Description¶

Importing necessary libraries¶

Loading the dataset¶

Data Overview¶

Exploratory Data Analysis (EDA)¶

Plotting histograms and boxplots for all the variables¶

Plotting all the features at one go¶

Data Pre-processing¶

Missing value imputation¶

Model Building¶

Model evaluation criterion¶

Defining scorer to be used for cross-validation and hyperparameter tuning¶

Model Building with original data¶

Observations¶

Model Building with Oversampled data¶

Observations¶

Model Building with Undersampled data¶

Observations¶

Models with Unersampled data and Over sampled data performed better than Orginal dataset. This is expected because the dataste was highly imbalanced¶

HyperparameterTuning¶

Sample Parameter Grids¶

Sample tuning method for Decision tree with original data¶

Sample tuning method for Decision tree with oversampled data¶

Sample tuning method for Decision tree with undersampled data¶

Model performance comparison and choosing the final model¶

Test set final performance¶

Observations¶

Pipelines to build the final model¶

Business Insights and Conclusions¶

	Accuracy	Recall	Precision	F1
Model_0	0.965588	0.489247	0.805310	0.608696
Model_1	0.979118	0.639785	0.967480	0.770227
Model_2	0.981176	0.709677	0.929577	0.804878
Model_3	0.979118	0.639785	0.967480	0.770227
Model_4	0.963529	0.446237	0.798077	0.572414
Model_5	0.989412	0.822581	0.980769	0.894737
Model_6	0.858529	0.838710	0.257002	0.393443
Model_7	0.936471	0.865591	0.457386	0.598513
Model_8	0.933824	0.854839	0.445378	0.585635

	Accuracy	Recall	Precision	F1
Model_0	0.965667	0.475610	0.821053	0.602317
Model_1	0.981000	0.658537	0.990826	0.791209
Model_2	0.986333	0.756098	0.992000	0.858131
Model_3	0.981000	0.658537	0.990826	0.791209
Model_4	0.966000	0.463415	0.844444	0.598425
Model_5	0.991000	0.841463	0.992806	0.910891
Model_6	0.866667	0.829268	0.267717	0.404762
Model_7	0.945333	0.896341	0.500000	0.641921
Model_8	0.944333	0.890244	0.494915	0.636166

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	...	V32	V33	V34	V35	V36	V37	V38	V39	V40	Target
0	-0.613489	-3.819640	2.202302	1.300420	-1.184929	-4.495964	-1.835817	4.722989	1.206140	-0.341909	...	2.291204	-5.411388	0.870073	0.574479	4.157191	1.428093	-10.511342	0.454664	-1.448363	0
1	0.389608	-0.512341	0.527053	-2.576776	-1.016766	2.235112	-0.441301	-4.405744	-0.332869	1.966794	...	-2.474936	2.493582	0.315165	2.059288	0.683859	-0.485452	5.128350	1.720744	-1.488235	0
2	-0.874861	-0.640632	4.084202	-1.590454	0.525855	-1.957592	-0.695367	1.347309	-1.732348	0.466500	...	-1.318888	-2.997464	0.459664	0.619774	5.631504	1.323512	-1.752154	1.808302	1.675748	0
3	0.238384	1.458607	4.014528	2.534478	1.196987	-3.117330	-0.924035	0.269493	1.322436	0.702345	...	3.517918	-3.074085	-0.284220	0.954576	3.029331	-1.367198	-3.412140	0.906000	-2.450889	0
4	5.828225	2.768260	-1.234530	2.809264	-1.641648	-1.406698	0.568643	0.965043	1.918379	-2.774855	...	1.773841	-1.501573	-2.226702	4.776830	-6.559698	-0.805551	-0.276007	-3.858207	-0.537694	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4995	-5.120451	1.634804	1.251259	4.035944	3.291204	-2.932230	-1.328662	1.754066	-2.984586	1.248633	...	9.979118	0.063438	0.217281	3.036388	2.109323	-0.557433	1.938718	0.512674	-2.694194	0
4996	-5.172498	1.171653	1.579105	1.219922	2.529627	-0.668648	-2.618321	-2.000545	0.633791	-0.578938	...	4.423900	2.603811	-2.152170	0.917401	2.156586	0.466963	0.470120	2.196756	-2.376515	0
4997	-1.114136	-0.403576	-1.764875	-5.879475	3.571558	3.710802	-2.482952	-0.307614	-0.921945	-2.999141	...	3.791778	7.481506	-10.061396	-0.387166	1.848509	1.818248	-1.245633	-1.260876	7.474682	0
4998	-1.703241	0.614650	6.220503	-0.104132	0.955916	-3.278706	-1.633855	-0.103936	1.388152	-1.065622	...	-4.100352	-5.949325	0.550372	-1.573640	6.823936	2.139307	-4.036164	3.436051	0.579249	0
4999	-0.603701	0.959550	-0.720995	8.229574	-1.815610	-2.275547	-2.574524	-1.041479	4.129645	-2.731288	...	2.369776	-1.062408	0.790772	4.951955	-7.440825	-0.069506	-0.918083	-2.291154	-5.362891	0

	Accuracy	Recall	Precision	F1
Model_0	0.9648	0.485816	0.815476	0.608889
Model_1	0.9790	0.645390	0.973262	0.776119
Model_2	0.9850	0.762411	0.964126	0.851485
Model_3	0.9790	0.645390	0.973262	0.776119
Model_4	0.9648	0.471631	0.831250	0.601810
Model_5	0.9904	0.843972	0.983471	0.908397
Model_6	0.9648	0.471631	0.831250	0.601810
Model_7	0.9412	0.875887	0.488142	0.626904
Model_8	0.9412	0.872340	0.488095	0.625954